---
title: "El dinero trae la felicidad? Analisis en paises Europeos"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    social: menu
    source_code: embed
runtime: shiny
---

```{r setup, include=FALSE}
library(flexdashboard)
library(ggrepel)
library(leaflet)
library(ggplot2)
library(dplyr)
library(readr)
library(rnaturalearth)
library(rnaturalearthdata)
library(RColorBrewer)
library(sf)
library(shiny)

# Leer los datos depurados
data_path <- 'C:/Users/abolsi/Reto2M8/Datos/Base de datos depurada/datos_clean.csv'
datos_dashboard <- read_csv(data_path)

# Invertir la escala de la variable 'health'
datos_dashboard <- datos_dashboard %>%
  mutate(inverted_health = 6 - health)

# Crear un vector de nombres de países mapeados a los códigos de país
country_map <- c(
  "AT" = "Austria",
  "BE" = "Belgium",
  "BG" = "Bulgaria",
  "CH" = "Switzerland",
  "CY" = "Cyprus",
  "CZ" = "Czechia",
  "DE" = "Germany",
  "DK" = "Denmark",
  "EE" = "Estonia",
  "ES" = "Spain",
  "FI" = "Finland",
  "FR" = "France",
  "GB" = "United Kingdom",
  "HR" = "Croatia",
  "HU" = "Hungary",
  "IE" = "Ireland",
  "IS" = "Iceland",
  "IT" = "Italy",
  "LT" = "Lithuania",
  "LV" = "Latvia",
  "ME" = "Montenegro",
  "NL" = "Netherlands",
  "NO" = "Norway",
  "PL" = "Poland",
  "PT" = "Portugal",
  "RS" = "Serbia",
  "SE" = "Sweden",
  "SI" = "Slovenia",
  "SK" = "Slovakia"
)

# Calcular la felicidad media, la salud media (invertida), el nivel de ingresos medio y la desigualdad de ingresos por país
summary_by_country <- datos_dashboard %>%
  group_by(cntry) %>%
  summarise(mean_happiness = mean(happy, na.rm = TRUE),
            mean_income = mean(hinctnta, na.rm = TRUE),
            mean_health = mean(inverted_health, na.rm = TRUE),
            income_sd = sd(hinctnta, na.rm = TRUE))

# Mapear los códigos de país a nombres de países reales
summary_by_country$country_name <- country_map[summary_by_country$cntry]

# Obtener el mapa de Europa
europe_map <- rnaturalearth::ne_countries(scale = "medium", continent = "Europe", returnclass = "sf")

# Unir los datos con el mapa usando country_name y name
europe_map <- europe_map %>%
  left_join(summary_by_country, by = c("name" = "country_name"))

# Calcular la desviación estándar de los ingresos, felicidad y salud por país
income_inequality <- datos_dashboard %>%
  group_by(cntry) %>%
  summarise(income_sd = sd(hinctnta, na.rm = TRUE),
            mean_happiness = mean(happy, na.rm = TRUE),
            mean_health = mean(health, na.rm = TRUE))

# Mapear los códigos de país a nombres de países reales
income_inequality$country_name <- country_map[income_inequality$cntry]

# Calcular la correlación entre desigualdad de ingresos y felicidad/salud
correlation_happiness <- cor(income_inequality$income_sd, income_inequality$mean_happiness, use = "complete.obs")
correlation_health <- cor(income_inequality$income_sd, income_inequality$mean_health, use = "complete.obs")

print(paste("Correlación entre desigualdad de ingresos y felicidad:", correlation_happiness))
print(paste("Correlación entre desigualdad de ingresos y salud:", correlation_health))


```

Análisis Geográfico
========================================================================

Column {data-width=650}
-----------------------------------------------------------------------

### Mapa Interactivo

```{r}
selectInput("variable", "Seleccione la variable a visualizar:",
              choices = c("Felicidad" = "mean_happiness", "Nivel de Ingresos" = "mean_income", "Salud" = "mean_health", "Desigualdad de Ingresos" = "income_sd"))

renderLeaflet({
  variable <- input$variable
  pal <- if (variable == "mean_happiness") {
    colorBin("Purples", domain = europe_map[[variable]], bins = 5, na.color = "transparent")
  } else if (variable == "mean_income") {
    colorBin("YlOrBr", domain = europe_map[[variable]], bins = 5, na.color = "transparent")
  } else if (variable == "mean_health") {
    colorBin("YlGn", domain = europe_map[[variable]], bins = 5, na.color = "transparent")
  } else {
    colorBin("Reds", domain = europe_map[[variable]], bins = 5, na.color = "transparent")
  }
  
  leaflet(data = europe_map) %>%
    addTiles() %>%
    setView(lng = 10, lat = 50, zoom = 5) %>%
    addPolygons(fillColor = ~pal(get(variable)),
                weight = 2,
                opacity = 1,
                color = "white",
                dashArray = "3",
                fillOpacity = 0.7,
                highlightOptions = highlightOptions(
                  weight = 5,
                  color = "#666",
                  dashArray = "",
                  fillOpacity = 0.7,
                  bringToFront = TRUE),
                label = ~paste(name, ": ", round(get(variable), 2))) %>%
    addLegend(pal = pal, values = ~get(variable), opacity = 0.7, title = variable,
              position = "bottomright")
})

leafletOutput("map")
```

Column {data-width=350}
----------------------------------------------------------------------

### Nivel de Ingresos vs. Felicidad


```{r}
income_threshold <- mean(summary_by_country$mean_income)
happiness_threshold <- mean(summary_by_country$mean_happiness)

ggplot(summary_by_country, aes(x = mean_income, y = mean_happiness, label = country_name)) +
  geom_point(aes(color = ifelse(mean_income > income_threshold & mean_happiness > happiness_threshold, "resaltado", "normal"))) +
  geom_text_repel(aes(color = ifelse(mean_income > income_threshold & mean_happiness > happiness_threshold, "resaltado", "normal"))) +
  labs(title = "Relación entre Nivel de Ingresos y Felicidad",
       x = "Nivel de Ingresos Medio",
       y = "Felicidad Promedio") +
  scale_color_manual(values = c("normal" = "black", "resaltado" = "purple")) +
  theme_minimal() +
  theme(legend.position = "none")
```


### Nivel de Ingresos vs. Salud


```{r}
health_threshold <- mean(summary_by_country$mean_health)

ggplot(summary_by_country, aes(x = mean_income, y = mean_health, label = country_name)) +
  geom_point(aes(color = ifelse(mean_income > income_threshold & mean_health > health_threshold, "resaltado", "normal"))) +
  geom_text_repel(aes(color = ifelse(mean_income > income_threshold & mean_health > health_threshold, "resaltado", "normal"))) +
  labs(title = "Relación entre Nivel de Ingresos y Salud",
       x = "Nivel de Ingresos Medio",
       y = "Salud Promedio") +
  scale_color_manual(values = c("normal" = "black", "resaltado" = "purple")) +
  theme_minimal() +
  theme(legend.position = "none")

```


Distribuciones
========================================================================

Column {data-width=250}
----------------------------------------------------------------------

### Distribución de Felicidad por País

```{r}
num_levels <- length(unique(datos_dashboard$happy))
color_palette <- colorRampPalette(brewer.pal(9, "Purples"))(num_levels)

ggplot(datos_dashboard, aes(x = factor(cntry), fill = factor(happy))) +
  geom_bar(position = "fill") +
  ggtitle("Distribución de Felicidad por País") +
  xlab("País") +
  ylab("Proporción") +
  labs(fill = "Felicidad") +
  scale_fill_manual(values = color_palette) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))


```


Column {data-width=250}
----------------------------------------------------------------------


### Distribución de Edades

```{r}
ggplot(datos_dashboard, aes(x = agea)) + geom_histogram() + ggtitle("Distribución de Edad")
```

### Distribución de Ingresos Familiares

```{r}
ggplot(datos_dashboard, aes(x = hinctnta)) + geom_histogram() + ggtitle("Distribución de Ingresos Familiares")
```

###  Distribución de Felicidad

```{r}
ggplot(datos_dashboard, aes(x = happy)) + geom_histogram() + ggtitle("Distribución de Felicidad")
```


Column {data-width=350}
----------------------------------------------------------------------


### Distribución de Niveles Educativos

```{r}
ggplot(datos_dashboard, aes(x = eisced)) + geom_histogram() + ggtitle("Distribución de Niveles Educativos")

```

### Distribución de Encuestrados por Pais
```{r}
ggplot(datos_dashboard, aes(x = cntry)) +
  geom_bar(stat = "count") +
  ggtitle("Distribución de Encuestrados por País") +
  xlab("País") +
  ylab("Número de Encuestrados") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))


```